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RZWQM - Root Zone Water Quality Model
Developer
Description
Water Quality Applications
Features
RZWQM Publications (PDF)
Applications of the Root Zone Water Quality Model (PDF)
Software Download (ARS Software site)
The Root Zone Water Quality Model (RZWQM) was developed in the 1990's by a team of USDA Agricultural Research Service (ARS) scientists. A majority of the team members are part of the present Rangeland Resources Systems Research Unit, Fort Collins, CO. Parts of the model have been revised and enhanced with cooperation of the ARS Northwest Watershed Research Laboratory, Boise, ID, and the ARS Nematode Research Laboratory, Tifton, GA. The next generation, RZWQM2 has been revised and enhanced to include the DSSAT 4.0 Cropping System Models with the cooperation of the University of Georgia and DSSAT modeling group. Additional crops and model enhancements for applications are done in cooperation with users nationally and internationally with the USDA ARS Agricultural System Research Unit RZWQM2 team.
Root Zone Water Quality Model 2 (RZWQM2) simulates major physical, chemical, and biological processes in an agricultural crop production system. RZWQM2 is a one-dimensional (vertical in the soil profile) process-based model that simulates the growth of the plant and the movement of water, nutrients and pesticides over, within and below the crop root zone of a unit area. It has a quasi-two-dimensional macropore/lateral flow. It responds to agricultural management practices including planting and harvest practices, tillage, pesticide, manure and chemical nutrient applications, and irrigation events. The model includes simulation of a tile drainage system. It has a Windows Interface (RZWQM2.EXE) with manages input and output for Projects and Scenarios and executes the science model (RZWQMrelease.exe). The science may also execute off ASCII file IO.
RZWQM2 may be as a tool for assessing the productivity of various cropping systems for various soil, weather and management conditions. Once calibrated and validated to the productivity of a cropping system for a climatic region, alternate soils, crop management scenarios may be tested for development of best management practices for the region with regards to crop productivity and environmental sustainability. Testing of these managements through historical climates can provide production probability distribution functions based on past climate patterns. Monthly Weather Modifiers are provided in RZWQM2 to test cropping system responses to increase or decrease in factors such as temperature, radiation, wind, relative humidity, and CO2. Management Modifiers allow users to game with application amounts. It is currently being tested for its adequacy to implement effects of climate change on systems.
Original focus and continued use of RZWQM2 is for assessing the environmental impact of alternative agricultural management strategies on the subsurface environment. These alternatives may include: conservation plans on field-by- filed basis; tillage and residue practices; crop rotations; planting date and density; and irrigation- , fertilizer-, and pesticide-scheduling (method of application, amounts and timing). The model predicts the effects of these management practices on the movement of nitrate and pesticides to runoff and deep percolation below the root zone. That is, the model predicts the potential for pollutant loadings to the groundwater thus allowing an assessment of nonpoint-source pollutant impacts on surface and ground water quality.
RZWQM2 consists of several major scientific sub models or processes including the DSSAT 4.0 Cropping system models that now define the simulation program. A Numerical Grid Generator for water and chemical transport movement and an Output Report Generator are part of the RZWQM2. The model generates three general output files with twenty-five optional debugging output files that provide detailed results generated by the model. The Output Report Generator uses model results to create summary tables and graphical output in 2- and 3-dimensional formats. The Windows Interface Interacts with the science via ASCII text files. The interface provides project and scenario management and execution with addition tool for the user to derive need input including additional Brooks Corey Parameter Estimation method, KSat estimation methods and weather file development. Users may use the interface and it tools to generate and manage these simulations or a commercial editor to run from individual scenario directories if the RZWQMrelease.exe science is located there.
- Physical processes include a large number of hydrologic processes; infiltration; chemical transport during infiltration; chemical transport to runoff during rainfall, water and chemical flow through soil matrix, micropores and macropores (i.e., root and worm channels), soil heat flow; fluctuating water table; tile drain, bare and residue-covered soil evaporation; crop transpiration; and soil water and chemical redistribution between rainfall and irrigation events. Snow accumulation and melt are also considered.
- Plant growth processes predict the relative response of plants to changes in environment. Environmental changes can be manifest either as normal variations in climatic variables or by differences in management practices. The model simulates carbon dioxide assimilation, carbon allocation, dark respiration, periodic tissue loss, plant mortality, root growth, water and nutrient (currently only N) uptake.
- Soil chemical processes consist of the soil inorganic environment in support of nutrient processes, chemical transport, and pesticide processes. The chemical state of the soil is characterized by soil pH, solution concentrations of the major ions, and adsorbed cations on the exchange complex. The model is capable of handling soil solution chemistry across a wide range of soil pH.
- Nutrient processes define carbon and nitrogen transformation within the soil profile. Given initial levels of soil humus, crop residues, other organics, and nitrate and ammonium concentrations, the model simulates mineralization, nitrification, immobilization, denitrification, and volatilization of appropriate nitrogen.
- Pesticide processes include the transformations and degradation of pesticides on plant surfaces, plant residue, the soil surface, and in soil profile. Given the plant, crop residue, soil and pesticide characteristics, coupled with environmental conditions, the model simulates the fate of pesticides above and within the soil. Adsorption coefficients are updated daily to account for variations in organic matter decomposition and bulk density changes. Degradation algorithms allow for 1st order, 2 compartment/ 1st order, specific pathway, and daughter product dissipation.
- Management processes consist of description of management activities influencing the state of the root zone. It includes tillage practices and the impacts on surface roughness, soil bulk density, and macroporosity; fertilizer, pesticide, and manure applications; crop planting; irrigation scheduling for flood furrow, sprinkler, and drip systems; and BMP algorithms for dynamic nitrogen-rate determination. Soil surface reconsolidation as a function of time, rainfall, and tillage. Decomposition and bioincorporation of surface residues as affected by water content and temperature, to describe ridge-tilled and no-tilled systems.
- DSSAT 4.0 Cropping System Models consist of both the CERES Maize and Wheat Models as well as the CROPGRO suite of models and the Substor Potato Model. All these models simulate the growth and development of 23 crop species with several varieties each. RZWQM2 provides a default DSSAT4.0 database but users often add customize default to their varieties via calibration and validation on their experimental data.
- Comparison Statistics for various outputs can be performed at both the project and scenario level. At the project level output from multiple scenarios can be compared or multiple variables for 1 scenario. RZWQM2 allows the entry of experimental observations in the expdata.dat file and performs observed versus predicted statistic for data present. Reviewing the RMSE of these comparisons assists the user in calibrating the model to their data.
